Associations between habitual diet, metabolic disease, and the gut microbiota using latent Dirichlet allocation

Dietary Fibre Modulates Gut Microbiota in Late Pregnancy Without Altering SCFA Levels, and Propionate Treatement Has No Effect on Placental Explant Function

Background/Objectives: Dietary fibre promotes health, partly by mediating gut microbiota and short-chain fatty acid (SCFA) production. Pregnancy alters the relationship between dietary composition and the gut microbiota, and it is unclear if fibre intake during late pregnancy alters the abundance of SCFA bacteria and circulating SFCA concentrations. The aim of this study was to determine the impact of dietary fibre on faecal microbiome composition and circulating concentrations of SCFA acetate, butyrate, and propionate in late pregnancy. We also aimed to assess the impact of propionate treatment on placental function using cultured placental explants. Methods: 16S rRNA gene amplicon sequencing was performed on faecal DNA collected at 28 weeks of gestation from participants enrolled in the SPRING cohort study consuming a low or adequate fibre diet. Circualting SCFA were assessed. Placental explants were treated with sodium propionate. Results: Fibre intake did not impact microbial diversity or richness but did impact the abundance of specific bacterial genera. Pregnant participants with low-fibre diets had a greater abundance of Bacteroides and Sutterella, and dietary fibre intake (mg/day) negatively correlated with genera, including Sutterella, Bilophila, and Bacteroides. SCFA concentrations did not differ between groups but circulating concentrations of acetate, propionate, and butyrate did correlate with the abundance of key bacterial genera. Propionate treatment of placental explants did not alter mRNA expression of fatty acid receptors, antioxidants, or markers of apoptosis, nor did it impact pAMPK levels. Conclusions: This study demonstrates that the impact of dietary fibre on SCFA concentrations in pregnant women is modest, although this relationship may be difficult to discern given that other dietary factors differed between groups. Furthermore, this study demonstrates that propionate does not impact key pathways in placental tissue, suggesting that previous associations between this SCFA and placental dysfunction may be due to other maternal factors.

Gut microbiota-derived imidazole propionate: an emerging target for the prevention and treatment of cardiometabolic diseases

Despite significant advancements in prevention and treatment, cardiometabolic diseases continue to pose a high burden of incidence and mortality. The chronic progression of these diseases necessitates the identification of early and complementary therapeutic targets to elucidate and mitigate residual risks in patient care. The gut microbiota acts as a sentinel between internal and external environments, transmitting modified risks associated with these factors to the host. Imidazole propionate (ImP), a histidine metabolite originating from the gut microbiota, gained attention after being found to impair glucose tolerance and insulin signaling several years ago. Epidemiological studies over the past five years have demonstrated a robust correlation between ImP and an increased risk of onset of type 2 diabetes (T2D) and obesity, exacerbation of kidney traits in chronic kidney disease (CKD), progression of atherosclerotic plaques, and elevated mortality rates in heart failure (HF). These findings suggest that ImP may serve as a pivotal target for the prevention and treatment of cardiometabolic diseases. Mechanistic insights have uncovered associations between ImP and insulin resistance, impaired glucose metabolism, chronic inflammation, and intestinal barrier damage. This review provides a comprehensive summary of the current evidence regarding the association between ImP and cardiometabolic impairment, highlighting its potential in advancing personalized approaches to disease prevention and management, and exploring the intricate interplay of diet, gut microbiota, and ImP in cardiovascular metabolic impairment. Overall, this review offers valuable insights into the multifaceted roles of ImP in cardiometabolic diseases, identifies current knowledge gaps, and discusses future research directions.

Relationship Between Depression and Epigallocatechin Gallate from the Perspective of Gut Microbiota: A Systematic Review

Depression, a serious mental illness, is characterized by high risk, high incidence, persistence, and tendency to relapse, posing a significant burden on global health. The connection between depression and gut microbiota is an emerging field of study in psychiatry and neuroscience. Understanding the gut-brain axis is pivotal for understanding the pathogenesis and treatment of depression. Gut microbes influence depression-like behaviors by impacting the hypothalamic-pituitary-adrenal axis (HPA), monoamine neurotransmitters, immune responses, cell signaling, and metabolic pathways. Tea, widely used in clinical practice to improve neuropsychiatric disorders, contains Epigallocatechin gallate (EGCG), a major ingredient of green tea, which effectively regulates intestinal flora. This review examined the risks and causes of depression, the complications associated with intestinal flora, their role in the development and treatment of depression, and how EGCG may alleviate depression through interactions with gut microbiota and other mechanisms.

Association between gut microbiota and short-chain fatty acids in children with obesity

The gut microbiome and its metabolites may be important role in regulating the pathogenesis of obesity. This study aimed to characterize the gut microbiome and short-chain fatty acid (SCFA) metabolome in obese children. This case-control study recruited children aged 7‒14 years and divided them into a normal group (NG) and an obese group (OG) based on their body mass index. Whole-genome shotgun metagenomic analysis was performed on fecal samples from the OG and NG groups to characterize the signatures and functional potential of the gut microbiota. Serum metabolite profiles were analyzed using high-performance liquid chromatography/mass spectrometry (LC/MS). The Statistical Package for the Social Sciences (SPSS, version 26) and R software were used for data analysis. A total of 99 children were recruited, with 49 in the OG and 50 in the NG. At the phylum level, Proteobacteria were significantly more abundant in children in the OG than those in the NG. At the genus level, Oscillibacter and Alistipes were significantly lower in children in the OG than those in the NG. Caproate levels significantly increased, whereas butyrate and isobutyrate levels decreased in children in the OG than those in the NG. Kyoto encyclopedia of genes and genomes (KEGG) functional analysis revealed 28 enriched KEGG pathways, of which/with the phosphotransferase system (PTS) and enhanced biofilm formation by Escherichia coli were particularly significant in the OG. Spearman's correlation analysis indicated that the genus Oscillibacter and species Clostridium_sp._CAG:302 connect serum metabolites and the gut microbiota in childhood obesity. Childhood obesity is correlated with the symbiotic status of the gut microbiota. The microbiota influences human metabolism via specific pathways, particularly butyrate, caproate, and the genus Oscillibacter, all closely associated with obesity.

The role of the microbiota-gut-brain axis and artificial intelligence in cognitive health of pediatric obstructive sleep apnea: A narrative review

Pediatric obstructive sleep apnea (OSA) is a prevalent sleep-related breathing disorder associated with significant neurocognitive and behavioral impairments. Recent studies have highlighted the role of gut microbiota and the microbiota-gut-brain axis (MGBA) in influencing cognitive health in children with OSA. This narrative review aims to summarize current knowledge on the relationship between gut microbiota, MGBA, and cognitive function in pediatric OSA. It also explores the potential of artificial intelligence and machine learning in advancing this field and identifying novel therapeutic strategies. Pediatric OSA is associated with gut dysbiosis, reduced microbial diversity, and metabolic disruptions. MGBA mechanisms, such as endocrine, immune, and neural pathways, link gut microbiota to cognitive outcomes. Artificial intelligence and machine learning methodologies offer promising tools to uncover microbial markers and mechanisms associated with cognitive deficits in OSA. Future research should focus on validating these findings through clinical trials and developing personalized therapeutic approaches targeting the gut microbiota.

Regulation of endotoxemia through the gut microbiota: The role of the Mediterranean diet and its components

Endotoxemia is closely related to many diseases. As the largest endotoxin reservoir in the human body, the gut microbiota should be a key target for alleviating endotoxemia. The intestinal microbiota is believed to cause endotoxemia directly or indirectly by modifying the intestinal barrier function through dysbiosis, changing intestinal mucosal permeability and bacterial translocation. Diet is known to be the main environmental factor affecting the intestinal microbiota, and different diets and food components have a large impact on the gut microbiota. The Mediterranean diet, which received much attention in recent years, is believed to be able to regulate the gut microbiota, thereby maintaining the function of the intestinal barrier and alleviating endotoxemia. In this review, we focus on the relationship between the gut microbiota and endotoxemia, and how the Mediterranean dietary (MD) pattern can interfere with endotoxemia through the gut microbiota.

Gut microbiome and NAFLD: impact and therapeutic potential

Non-Alcoholic Fatty Liver Disease (NAFLD) affects approximately 32.4% of the global population and poses a significant health concern. Emerging evidence underscores the pivotal role of the gut microbiota-including bacteria, viruses, fungi, and parasites-in the development and progression of NAFLD. Dysbiosis among gut bacteria alters key biological pathways that contribute to liver fat accumulation and inflammation. The gut virome, comprising bacteriophages and eukaryotic viruses, significantly shapes microbial community dynamics and impacts host metabolism through complex interactions. Similarly, gut fungi maintain a symbiotic relationship with bacteria; the relationship between gut fungi and bacteria is crucial for overall host health, with certain fungal species such as Candida in NAFLD patients showing detrimental associations with metabolic markers and liver function. Additionally, the "hygiene hypothesis" suggests that reduced exposure to gut parasites may affect immune regulation and metabolic processes, potentially influencing conditions like obesity and insulin resistance. This review synthesizes current knowledge on the intricate interactions within the gut microbiota and their associations with NAFLD. We highlight the therapeutic potential of targeting these microbial communities through interventions such as probiotics, prebiotics, and fecal microbiota transplantation. Addressing the complexities of NAFLD requires comprehensive strategies that consider the multifaceted roles of gut microorganisms in disease pathology.

Influence of Peanut Consumption on the Gut Microbiome: A Randomized Clinical Trial

Background: Peanut consumption could impact cardiometabolic health through gut microbiota, a hypothesis that remains to be investigated. A randomized clinical trial in Vietnam evaluated whether peanut consumption alters gut microbiome communities. Methods: One hundred individuals were included and randomly assigned to the peanut intervention and control groups. A total of 51 participants were provided with and asked to consume 50 g of peanuts daily, while 49 controls maintained their usual dietary intake for 16 weeks. Stool samples were collected before and on the last day of the trial. After excluding 22 non-compliant participants and those who received antibiotic treatment, 35 participants from the intervention and 43 from the control were included in the analysis. Gut microbiota composition was measured by shotgun metagenomic sequencing. Associations of changes in gut microbial diversity with peanut intervention were evaluated via linear regression analysis. Linear mixed-effects models were used to analyze associations of composition, sub-community structure, and microbial metabolic pathways with peanut intervention. We also performed beta regression analysis to examine the impact of peanut intervention on the overall and individual stability of microbial taxa and metabolic pathways. All associations with false discovery rate (FDR)-corrected p-values of <0.1 were considered statistically significant. Results: No significant changes were found in α- and β-diversities and overall gut microbial stability after peanut intervention. However, the peanut intervention led to lower enrichment of five phyla, five classes, two orders, twenty-four metabolic pathways, and six species-level sub-communities, with a dominant representation of Bifidobacterium pseudocatenulatum, Escherichia coli D, Holdemanella biformis, Ruminococcus D bicirculans, Roseburia inulinivorans, and MGYG-HGUT-00200 (p < 0.05 and FDR < 0.1). The peanut intervention led to the short-term stability of several species, such as Faecalibacterium prausnitzii F and H, and a metabolic pathway involved in nitrate reduction V (p < 0.05; FDR < 0.1), known for their potential roles in human health, especially cardiovascular health. Conclusions: In summary, a 16-week peanut intervention led to significant changes in gut microbial composition, species-level sub-communities, and the short-term stability of several bacteria, but not overall gut microbial diversity and stability. Further research with a larger sample size and a longer intervention period is needed to confirm these findings and investigate the direct impact of gut-microbiome-mediated health effects of peanut consumption. Trial registration: The International Traditional Medicine Clinical Trial Registry (ITMCTR). Registration number: ITMCTR2024000050. Retrospectively Registered 24 April 2024.

Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing

Verbal autopsy (VA) narratives play a crucial role in understanding and documenting the causes of mortality, especially in regions lacking robust medical infrastructure. In this study, we propose a comprehensive approach to extract mortality causes and identify prevalent diseases from VA narratives utilizing advanced text mining techniques, so as to better understand the underlying health issues leading to mortality. Our methodology integrates n-gram-based language processing, Latent Dirichlet Allocation (LDA), and BERTopic, offering a multi-faceted analysis to enhance the accuracy and depth of information extraction. This is a retrospective study that uses secondary data analysis. We used data from the Agincourt Health and Demographic Surveillance Site (HDSS), which had 16338 observations collected between 1993 and 2015. Our text mining steps entailed data acquisition, pre-processing, feature extraction, topic segmentation, and discovered knowledge. The results suggest that the HDSS population may have died from mortality causes such as vomiting, chest/stomach pain, fever, coughing, loss of weight, low energy, headache. Additionally, we discovered that the most prevalent diseases entailed human immunodeficiency virus (HIV), tuberculosis (TB), diarrhoea, cancer, neurological disorders, malaria, diabetes, high blood pressure, chronic ailments (kidney, heart, lung, liver), maternal and accident related deaths. This study is relevant in that it avails valuable insights regarding mortality causes and most prevalent diseases using novel text mining approaches. These results can be integrated in the diagnosis pipeline for ease of human annotation and interpretation. As such, this will help with effective informed intervention programmes that can improve primary health care systems and chronic based delivery, thus increasing life expectancy.

An energy landscape approach reveals the potential key bacteria contributing to the development of inflammatory bowel disease

The dysbiosis of microbiota has been reported to be associated with numerous human pathophysiological processes, including inflammatory bowel disease (IBD). With advancements in high-throughput sequencing, various methods have been developed to study the alteration of microbiota in the development and progression of diseases. However, a suitable approach to assess the global stability of the microbiota in disease states through time-series microbiome data is yet to be established. In this study, we have introduced a novel Energy Landscape construction method, which incorporates the Latent Dirichlet Allocation (LDA) model and the pairwise Maximum Entropy (MaxEnt) model for their complementary advantages, and demonstrate its utility by applying it to an IBD time-series dataset. Through this approach, we obtained the microbial assemblages' energy profile of the whole microbiota under the IBD condition and uncovered the hidden stable stages of microbiota structure during the disease development with time-series microbiome data. The Bacteroides-dominated assemblages presenting in multiple stable states suggest the potential contribution of Bacteroides and interactions with other microbial genera, like Alistipes, and Faecalibacterium, to the development of IBD. Our proposed method provides a novel and insightful tool for understanding the alteration and stability of the microbiota under disease states and offers a more holistic view of the complex dynamics at play in microbiota-mediated diseases.

A universal plasma metabolites-derived signature predicts cardiovascular disease risk in MAFLD

BACKGROUND

Metabolic associated fatty liver disease (MAFLD) is a novel concept proposed in 2020, which is more practical for identifying patients with fatty liver disease with high risk of disease progression. Fatty liver is a driver for extrahepatic complications, particularly cardiovascular diseases (CVD). Although the risk of CVD in MAFLD could be predicted by carotid ultrasound test, a very early stage prediction method before the formation of pathological damage is still lacking.

METHODS

Stool microbiomes and plasma metabolites were compared across 196 well-characterized participants encompassing normal controls, simple MAFLD patients, MAFLD patients with carotid artery pathological changes, and MAFLD patients with diagnosed coronary artery disease (CAD). 16S rDNA sequencing data and untargeted metabolomic profiles were interrogatively analyzed using differential abundance analysis and random forest (RF) machine learning algorithm to identify discriminatory gut microbiomes and metabolomic.

RESULTS

Characteristic microbial changes in MAFLD patients with CVD risk were represented by the increase of Clostridia and Firmicutes-to-Bacteroidetes ratios. Faecalibacterium was negatively correlated with mean-intima-media thickness (IMT), TC, and TG. Megamonas, Bacteroides, Parabacteroides, and Escherichia were positively correlated with the exacerbation of pathological indexes. MAFLD patients with CVD risk were characterized by the decrease of lithocholic acid taurine conjugate, and the increase of ethylvanillin propylene glycol acetal, both of which had close relationship with Ruminococcus and Gemmiger. Biotin l-sulfoxide had positive correlation with mean-IMT, TG, and weight. The general auxin pesticide beta-naphthoxyacetic acid and the food additive glucosyl steviol were both positively correlated with the increase of mean-IMT. The model combining the metabolite signatures with 9 clinical parameters accurately distinguished MAFLD with CVD risk in the proband and validation cohort. It was found that citral was the most important discriminative metabolite marker, which was validated by both in vitro and in vivo experiments.

CONCLUSIONS

Simple MAFLD patients and MAFLD patients with CVD risk had divergent gut microbes and plasma metabolites. The predictive model based on metabolites and 9 clinical parameters could effectively discriminate MAFLD patients with CVD risk at a very early stage.

Diet Mediate the Impact of Host Habitat on Gut Microbiome and Influence Clinical Indexes by Modulating Gut Microbes and Serum Metabolites

The impact of external factors on the human gut microbiota and how gut microbes contribute to human health is an intriguing question. Here, the gut microbiome of 3,224 individuals (496 with serum metabolome) with 109 variables is studied. Multiple analyses reveal that geographic factors explain the greatest variance of the gut microbiome and the similarity of individuals' gut microbiome is negatively correlated with their geographic distance. Main food components are the most important factors that mediate the impact of host habitats on the gut microbiome. Diet and gut microbes collaboratively contribute to the variation of serum metabolites, and correlate to the increase or decrease of certain clinical indexes. Specifically, systolic blood pressure is lowered by vegetable oil through increasing the abundance of Blautia and reducing the serum level of 1-palmitoyl-2-palmitoleoyl-GPC (16:0/16:1), but it is reduced by fruit intake through increasing the serum level of Blautia improved threonate. Besides, aging-related clinical indexes are also closely correlated with the variation of gut microbes and serum metabolites. In this study, the linkages of geographic locations, diet, the gut microbiome, serum metabolites, and physiological indexes in a Chinese population are characterized. It is proved again that gut microbes and their metabolites are important media for external factors to affect human health.

Integrative metabolomics highlights gut microbiota metabolites as novel NAFLD-related candidate biomarkers in children

Altered gut microbiota and metabolites are important for non-alcoholic fatty liver disease (NAFLD) in children. We aimed to comprehensively examine the effects of gut metabolites on NAFLD progression. We performed integrative metabolomics (untargeted discovery and targeted validation) analysis of non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), and obesity in children. Fecal samples were collected from 75 subjects in the discovery cohort (25 NAFL, 25 NASH, and 25 obese control children) and 145 subjects in an independent validation cohort (53 NAFL, 39 NASH, and 53 obese control children). Among 2,491 metabolites, untargeted metabolomics revealed a complete NAFLD metabolic map containing 318 increased and 123 decreased metabolites. Then, machine learning selected 65 important metabolites that can distinguish the severity of the NAFLD. Furthermore, precision-targeted metabolomics selected 5 novel gut metabolites from 20 typical metabolites. The functionality of candidate metabolites was validated in hepatocyte cell lines. In the end, this study annotated two novel elevated pathogenic metabolites (dodecanoic acid and creatinine) and a relationship between depleted protective gut microbiota (Butyricicoccus and Alistipes), increased inflammation (IL-1β), lipid metabolism (TG), and liver function (ALT and AST). This study demonstrates the role of novel gut metabolites (dodecanoic acid and creatinine), as the fatty acid metabolism regulator contributing to NAFLD development through its influence on inflammation and liver function.

IMPORTANCE

Altered gut microbiota and metabolites are a major cause of non-alcoholic fatty liver disease (NAFLD) in children. This study demonstrated a complete gut metabolic map of children with NAFLD, containing 318 increased and 123 decreased metabolites by untargeted metabolomic. Multiple validation approaches (machine learning and targeted metabolomic) selected five novel gut metabolites for targeted metabolomics, which can distinguish NAFLD status and severity. The gut microbiota (Butyricicoccus and Alistipes) and metabolites (creatinine and dodecanoic acid) were novel biomarkers associated with impaired liver function and inflammation and validated by experiments of hepatocyte cell lines. The data provide a better understanding of the importance of gut microbiota and metabolite alterations in NAFLD, which implies that the altered gut microbiota and metabolites may represent a potential target to prevent NAFLD development.

Characteristics of Gut Microbiota in Patients with Erectile Dysfunction: A Chinese Pilot Study

PURPOSE

Little is known about the role of gut microbiota in the pathogenesis of erectile dysfunction (ED). We performed a study to compare taxonomic profiles of gut microbiota of ED and healthy males.

MATERIALS AND METHODS

A total of 43 ED patients and 16 healthy controls were enrolled in the study. The 5-item version of the International Index of Erectile Function (IIEF-5) with a cutoff value of 21 was used to evaluate erectile function. All participants underwent nocturnal penile tumescence and rigidity test. Samples of stool were sequenced to determine the gut microbiota.

RESULTS

We identified a distinct beta diversity of gut microbiome in ED patients by unweighted UniFrac analysis (R²=0.026, p=0.036). Linear discriminant analysis effect size (LEfse) analysis showed Actinomyces was significantly enriched, whereas Coprococcus_1, Lachnospiraceae_FCS020_group, Lactococcus, Ruminiclostridium_5, and Ruminococcaceae_UCG_002 were depleted in ED patients. Actinomyces showed a significant negative correlation with the duration of qualified erection, average maximum rigidity of tip, average maximum rigidity of base, tip tumescence activated unit (TAU), and base TAU. Coprococcus_1, Lachnospiraceae_FCS020_group, Ruminiclostridium_5, and Ruminococcaceae_UCG_002 were significantly correlated with the IIEF-5 score. Ruminiclostridium_5 and Ruminococcaceae_UCG_002 were positively related with average maximum rigidity of tip, average maximum rigidity of base, ΔTumescence of tip, and Tip TAU. Further, a random forest classifier based on the relative abundance of taxa showed good diagnostic efficacy with an area under curve of 0.72.

CONCLUSIONS

This pilot study identified evident alterations in the gut microbiome composition of ED patients and found Actinomyces was negatively correlated with erectile function, which may be a key pathogenic bacteria.

Double-blinded, randomized clinical trial of Gegen Qinlian decoction pinpoints Faecalibacterium as key gut bacteria in alleviating hyperglycemia

Background

Accumulating evidence suggests that metabolic disorders, including type 2 diabetes mellitus (T2DM), can be treated with traditional Chinese medicine formulas, such as the Gegen Qinlian decoction (GQD). This study elucidates the mechanisms by which gut microbes mediate the anti-diabetic effects of GQD.

Methods

We conducted a double-blind randomized clinical trial involving 120 untreated participants with T2DM. During the 12-week intervention, anthropometric measurements and diabetic traits were recorded every 4 weeks. Fecal microbiota and serum metabolites were measured before and after the intervention using 16S rDNA sequencing, liquid chromatography-mass spectrometry, and Bio-Plex panels.

Results

Anti-diabetic effects were observed in the GQD group in the human trial. Specifically, glycated hemoglobin, fasting plasma glucose, and two-hour postprandial blood glucose levels were significantly lower in the GQD group than in the placebo group. Additionally, Faecalibacterium was significantly enriched in the GQD group, and the short-chain fatty acid levels were higher and the serum inflammation-associated marker levels were lower in the GQD group compared to the placebo group. Moreover, Faecalibacterium abundance negatively correlated with the levels of serum hemoglobin, fasting plasma glucose, and pro-inflammatory cytokines. Finally, the diabetes-alleviating effect of Faecalibacterium was confirmed by oral administration of Faecalibacterium prausnitzii (DSMZ 17677) in T2DM mouse model.

Conclusions

GQD improved type 2 diabetes primarily by modulating the abundance of Faecalibacterium in the gut microbiota, alleviating metabolic disorders and the inflammatory state.

Trial registration

Registry No. ChiCTR-IOR-15006626.

A new approach to describe the taxonomic structure of microbiome and its application to assess the relationship between microbial niches

BACKGROUND

Data from microbiomes from multiple niches is often collected, but methods to analyse these often ignore associations between niches. One interesting case is that of the oral microbiome. Its composition is receiving increasing attention due to reports on its associations with general health. While the oral cavity includes different niches, multi-niche microbiome data analysis is conducted using a single niche at a time and, therefore, ignores other niches that could act as confounding variables. Understanding the interaction between niches would assist interpretation of the results, and help improve our understanding of multi-niche microbiomes.

METHODS

In this study, we used a machine learning technique called latent Dirichlet allocation (LDA) on two microbiome datasets consisting of several niches. LDA was used on both individual niches and all niches simultaneously. On individual niches, LDA was used to decompose each niche into bacterial sub-communities unveiling their taxonomic structure. These sub-communities were then used to assess the relationship between microbial niches using the global test. On all niches simultaneously, LDA allowed us to extract meaningful microbial patterns. Sets of co-occurring operational taxonomic units (OTUs) comprising those patterns were then used to predict the original location of each sample.

RESULTS

Our approach showed that the per-niche sub-communities displayed a strong association between supragingival plaque and saliva, as well as between the anterior and posterior tongue. In addition, the LDA-derived microbial signatures were able to predict the original sample niche illustrating the meaningfulness of our sub-communities. For the multi-niche oral microbiome dataset we had an overall accuracy of 76%, and per-niche sensitivity of up to 83%. Finally, for a second multi-niche microbiome dataset from the entire body, microbial niches from the oral cavity displayed stronger associations to each other than with those from other parts of the body, such as niches within the vagina and the skin.

CONCLUSION

Our LDA-based approach produces sets of co-occurring taxa that can describe niche composition. LDA-derived microbial signatures can also be instrumental in summarizing microbiome data, for both descriptions as well as prediction.

Microbiota-accessible fiber activates short-chain fatty acid and bile acid metabolism to improve intestinal mucus barrier in broiler chickens

IMPORTANCE

The intestinal mucus barrier, located at the interface of the intestinal epithelium and the microbiota, is the first line of defense against pathogenic microorganisms and environmental antigens. Dietary polysaccharides, which act as microbiota-accessible fiber, play a key role in the regulation of intestinal microbial communities. However, the mechanism via which dietary fiber affects the intestinal mucus barrier through targeted regulation of the gut microbiota is not clear. This study provides fundamental evidence for the benefits of dietary fiber supplementation in broiler chickens through improvement in the intestinal mucus barrier by targeted regulation of the gut ecosystem. Our findings suggest that the microbiota-accessible fiber-gut microbiota-short-chain fatty acid/bile acid axis plays a key role in regulating intestinal function.

Brassica oleracea L. extract ameliorates isoproterenol-induced myocardial injury by regulating HIF-1α-mediated glycolysis

Brassica oleracea L. (BO) is an important vegetable with proven health benefits. This study aimed to elucidate the constituents of BO leaf extract (BOE) and evaluate its effect on myocardial injury. For this purpose, the constituents of BOE were identified using ultra-high performance liquid chromatography with quadrupole time-of- flight mass spectrometry, and 26 compounds were determined, including glucosinolates, sulfur compounds, alkaloids, phenolic acids, flavones, and two other kinds of compounds. The effects of BOE on myocardial cells were evaluated using isoproterenol (ISO)-treated H9C2 cells and Wistar rats, and the results revealed that BOE could inhibit cardiomyocyte hypertrophy and reduce the levels of B-type natriuretic peptide, nitric oxide, reactive oxygen species, lactic acid, and pyruvic acid. Meanwhile, BOE could increase the levels of mitochondrial membrane potential. Moreover, BOE could reduce the levels of apoptosis- and glycolysis-related proteins. Taken together, our data demonstrated that BOE treatment could alleviate ISO-induced myocardial cell injury by downregulating apoptosis and glycolysis signals.

Unsupervised learning for medical data: A review of probabilistic factorization methods

We review popular unsupervised learning methods for the analysis of high-dimensional data encountered in, for example, genomics, medical imaging, cohort studies, and biobanks. We show that four commonly used methods, principal component analysis, K-means clustering, nonnegative matrix factorization, and latent Dirichlet allocation, can be written as probabilistic models underpinned by a low-rank matrix factorization. In addition to highlighting their similarities, this formulation clarifies the various assumptions and restrictions of each approach, which eases identifying the appropriate method for specific applications for applied medical researchers. We also touch upon the most important aspects of inference and model selection for the application of these methods to health data.

Gut microbiome diversity, variability, and latent community types compared with shifts in body weight during the freshman year of college in dormitory-housed adolescents

Significant human gut microbiome changes during adolescence suggest that microbial community evolution occurs throughout important developmental periods including the transition to college, a typical life phase of weight gain. In this observational longitudinal study of 139 college freshmen living in on-campus dormitories, we tracked changes in the gut microbiome via 16S amplicon sequencing and body weight across a single academic year. Participants were grouped by weight change categories of gain (WG), loss (WL), and maintenance (WM). Upon assessment of the community structure, unweighted and weighted UniFrac metrics revealed significant shifts with substantial variation explained by individual effects within weight change categories. Genera that positively contributed to these associations with weight change included Bacteroides, Blautia, and Bifidobacterium in WG participants and Prevotella and Faecalibacterium in WL and WM participants. Moreover, the Prevotella/Bacteroides ratio was significantly different by weight change category, with WL participants displaying an increased ratio. Importantly, these genera did not display co-dominance nor ease of transition between Prevotella- and Bacteroides-dominated states. We further assessed the overall taxonomic variation, noting the increased stability of the WL compared to the WG microbiome. Finally, we found 30 latent community structures within the microbiome with significant associations with waist circumference, sleep, and dietary factors, with alcohol consumption chief among them. Our findings highlight the high level of individual variation and the importance of initial gut microbiome community structure in college students during a period of major lifestyle changes. Further work is needed to confirm these findings and explore mechanistic relationships between gut microbes and weight change in free-living individuals.

Nutri-microbiome epidemiology, an emerging field to disentangle the interplay between nutrition and microbiome for human health

Diet and nutrition have a substantial impact on the human microbiome, and interact with the microbiome, especially gut microbiome, to modulate various diseases and health status. Microbiome research has also guided the nutrition field to a more integrative direction, becoming an essential component of the rising area of precision nutrition. In this review, we provide a broad insight into the interplay among diet, nutrition, microbiome, and microbial metabolites for their roles in the human health. Among the microbiome epidemiological studies regarding the associations of diet and nutrition with microbiome and its derived metabolites, we summarize those most reliable findings and highlight evidence for the relationships between diet and disease-associated microbiome and its functional readout. Then, the latest advances of the microbiome-based precision nutrition research and multidisciplinary integration are described. Finally, we discuss several outstanding challenges and opportunities in the field of nutri-microbiome epidemiology.

Targeting microbiota in dietary obesity management: a systematic review on randomized control trials in adults

Obesity is an alarming public health problem. Tailored nutritional therapy is advisable since emerging evidence on complex cross-talks among multifactorial agents. In this picture, the gut microbiota is highly individualized and intricately dependent on dietary patterns, with implications for obesity management. Most of the papers on the topic are observational and often conflicting. This review aimed to systematically organize the body of evidence on microbiota deriving from dietary trials in adult obesity giving the most certain phylogenetic, and metabolomic signatures in relation to both the host metabolism and phenotype changes published until now. We retrieved 18 randomized control trials on 1385 subjects with obesity who underwent several dietary interventions, including standard diet and healthy dietary regimens. Some phyla and species were more related to diets rich in fibers and others to healthy diets. Weight loss, metabolism improvements, inflammatory markers decrease were specifically related to different microorganisms or functions. The Prevotella/Bacteroides ratio was one of the most reported predictors. People with the burden of obesity comorbidities had the most significant taxonomic changes in parallel with a general improvement. These data emphasize the possibility of using symbiotic approaches involving tailored diets, microbiota characteristics, and maybe drugs to treat obesity and metabolic disorders. We encourage Authors to search for specific phylogenetic associations beyond a too generally reported Firmicutes/Bacteroides ratio.

Oral microbiome and ischemic stroke risk among elderly Chinese women

Background

Stroke, a leading cause of disability worldwide, has been associated with periodontitis. However, whether stroke risk is related to oral microbiota remains unknown. This study aims to evaluate the associations between the oral microbiome and ischemic stroke risk.

Methods

In a case-control study of 134 case-control pairs nested within a prospective cohort study, we examined pre-diagnostic oral microbiome in association with stroke risk via shotgun metagenomic sequencing. The microbial sub-community and functional profiling were performed using Latent Dirichlet Allocation and HUMAnN2. Associations of microbial diversity, sub-community structure, and individual microbial features with ischemic stroke risk were evaluated via conditional logistic regression.

Results

Alpha and beta diversities differ significantly between cases and controls. One genus- and two species-level sub-communities were significantly associated with decreased ischemic stroke risk, with odds ratios (95% confidence intervals) of 0.52 (0.31-0.90), 0.51 (0.31-0.84), and 0.60 (0.36-0.99), respectively. These associations were potentially driven by the representative taxa in these sub-communities, i.e., genus Corynebacterium and Lautropia, and species Lautropia mirabilis and Neisseria elongate (p < 0.05). Additionally, 55 taxa, 1,237 gene families, and 90 metabolic pathways were associated with ischemic stroke risk at p < 0.05.

Conclusion

Our study highlights the role of oral microbiota in the etiology of ischemic stroke and calls for further research.

Gut microbiota and inflammation patterns for specialized athletes: a multi-cohort study across different types of sports

Regular high-intensity exercise can cause changes in athletes' gut microbiota, and the extent and nature of these changes may be affected by the athletes' exercise patterns. However, it is still unclear to what extent different types of athletes have distinct gut microbiome profiles and whether we can effectively monitor an athlete's inflammatory risk based on their microbiota. To address these questions, we conducted a multi-cohort study of 543 fecal samples from athletes in three different sports: aerobics (n = 316), wrestling (n = 53), and rowing (n = 174). We sought to investigate how athletes' gut microbiota was specialized for different types of sports, and its associations with inflammation, diet, anthropometrics, and anaerobic measurements. We established a microbiota catalog of multi-cohort athletes and found that athletes have specialized gut microbiota specific to the type of sport they engaged in. Using latent Dirichlet allocation, we identified 10 microbial subgroups of athletes' gut microbiota, each of which had specific correlations with inflammation, diet, and anaerobic performance in different types of athletes. Notably, most inflammation indicators were associated with Prevotella-driven subgroup 7. Finally, we found that the effects of sport types and exercise intensity on the gut microbiota were sex-dependent. These findings shed light on the complex associations between physical factors, gut microbiota, and inflammation in athletes of different sports types and could have significant implications for monitoring potential inflammation risk and developing personalized exercise programs. IMPORTANCE This study is the first multi-cohort investigation of athletes across a range of sports, including aerobics, wrestling, and rowing, with the goal of establishing a multi-sport microbiota catalog. Our findings highlight that athletes' gut microbiota is sport-specific, indicating that exercise patterns may play a significant role in shaping the microbiome. Additionally, we observed distinct associations between gut microbiota and markers of inflammation, diet, and anaerobic performance in athletes of different sports. Moreover, we expanded our analysis to include a non-athlete cohort and found that exercise intensity had varying effects on the gut microbiota of participants, depending on sex.

Topic modeling for multi-omic integration in the human gut microbiome and implications for Autism

While healthy gut microbiomes are critical to human health, pertinent microbial processes remain largely undefined, partially due to differential bias among profiling techniques. By simultaneously integrating multiple profiling methods, multi-omic analysis can define generalizable microbial processes, and is especially useful in understanding complex conditions such as Autism. Challenges with integrating heterogeneous data produced by multiple profiling methods can be overcome using Latent Dirichlet Allocation (LDA), a promising natural language processing technique that identifies topics in heterogeneous documents. In this study, we apply LDA to multi-omic microbial data (16S rRNA amplicon, shotgun metagenomic, shotgun metatranscriptomic, and untargeted metabolomic profiling) from the stool of 81 children with and without Autism. We identify topics, or microbial processes, that summarize complex phenomena occurring within gut microbial communities. We then subset stool samples by topic distribution, and identify metabolites, specifically neurotransmitter precursors and fatty acid derivatives, that differ significantly between children with and without Autism. We identify clusters of topics, deemed "cross-omic topics", which we hypothesize are representative of generalizable microbial processes observable regardless of profiling method. Interpreting topics, we find each represents a particular diet, and we heuristically label each cross-omic topic as: healthy/general function, age-associated function, transcriptional regulation, and opportunistic pathogenesis.

Enterosignatures define common bacterial guilds in the human gut microbiome

The human gut microbiome composition is generally in a stable dynamic equilibrium, but it can deteriorate into dysbiotic states detrimental to host health. To disentangle the inherent complexity and capture the ecological spectrum of microbiome variability, we used 5,230 gut metagenomes to characterize signatures of bacteria commonly co-occurring, termed enterosignatures (ESs). We find five generalizable ESs dominated by either Bacteroides, Firmicutes, Prevotella, Bifidobacterium, or Escherichia. This model confirms key ecological characteristics known from previous enterotype concepts, while enabling the detection of gradual shifts in community structures. Temporal analysis implies that the Bacteroides-associated ES is "core" in the resilience of westernized gut microbiomes, while combinations with other ESs often complement the functional spectrum. The model reliably detects atypical gut microbiomes correlated with adverse host health conditions and/or the presence of pathobionts. ESs provide an interpretable and generic model that enables an intuitive characterization of gut microbiome composition in health and disease.

Regulation of SARS-CoV-2 infection by diet-modulated gut microbiota

Coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 infection has claimed millions of lives since late 2019, yet there are still many unexplored areas in its pathogenesis and clinical outcomes. COVID-19 is a disease that can affects multiple systems, some of which are overlapped with those modulated by gut microbiota, especially the immune system, thus leading to our concentration on analyzing the roles of microbiota in COVID-19 pathogenesis through the gut-lung axis. Dysbiosis of the commensal intestinal microbes and their metabolites (e.g., SCFAs) as well as the expression and activity of ACE2 in the gut could influence the host's immune system in COVID-19 patients. Moreover, it has been known that the elderly and individuals diagnosed with comorbidities (e.g., hypertension, type 2 diabetes mellitus, cardiovascular disease, etc.) are more susceptible to gut flora alterations, SARS-CoV-2 infection, and death. Thus, in this review we will focus on analyzing how the gut microbiota regulates the immune system that leads to different responses to SARS-CoV-2 infection. Since diet is a major factor to modulate the status of gut microbiota, dietary influence on COVID-19 pathogenesis will be also discussed, aiming to shed light on how diet-modulated gut microbiota regulates the susceptibility, severity, and treatment of SARS-CoV-2 infection.

Latent Dirichlet Allocation modeling of environmental microbiomes

Interactions between stressed organisms and their microbiome environments may provide new routes for understanding and controlling biological systems. However, microbiomes are a form of high-dimensional data, with thousands of taxa present in any given sample, which makes untangling the interaction between an organism and its microbial environment a challenge. Here we apply Latent Dirichlet Allocation (LDA), a technique for language modeling, which decomposes the microbial communities into a set of topics (non-mutually-exclusive sub-communities) that compactly represent the distribution of full communities. LDA provides a lens into the microbiome at broad and fine-grained taxonomic levels, which we show on two datasets. In the first dataset, from the literature, we show how LDA topics succinctly recapitulate many results from a previous study on diseased coral species. We then apply LDA to a new dataset of maize soil microbiomes under drought, and find a large number of significant associations between the microbiome topics and plant traits as well as associations between the microbiome and the experimental factors, e.g. watering level. This yields new information on the plant-microbial interactions in maize and shows that LDA technique is useful for studying the coupling between microbiomes and stressed organisms.

Effect of a Co-Feed Liquid Whey-Integrated Diet on Crossbred Pigs' Fecal Microbiota

This study assessed the potential effect of a co-feed liquid whey-integrated diet on the fecal microbiota of 14 crossbred pigs. The experimental design was as follows: seven pigs were in the control group, fed with a control feed, and seven were in the experimental group, fed with the same control feed supplemented daily with liquid whey. The collection of fecal samples was conducted on each animal before the dietary treatment (T0) and one (T1), and two (T2) months after the beginning of the co-feed integration. In addition, blood samples were collected from each pig at the same time points in order to evaluate the physiological parameters. Taxonomic analysis showed a bacterial community dominated by Firmicutes, Bacteroidetes, Spirochaetes, and Proteobacteria phyla that populated the crossbred pig feces. The diversity metrics suggested that the co-feed supplementation affected some alpha diversity indexes of the fecal microbiota. In addition, the differential abundance analysis at the genus level revealed significant differences for various genera, suggesting that the liquid whey supplementation potentially influenced a part of the bacterial community over time. Spearman's correlations revealed that the differential abundant genera identified are positively or negatively correlated with the physiological parameters.

Healthy dietary patterns are associated with the gut microbiome in the Hispanic Community Health Study/Study of Latinos

BACKGROUND

Dietary patterns high in healthy minimally processed plant foods play an important role in modulating the gut microbiome and promoting cardiometabolic health. Little is known on the diet-gut microbiome relationship in US Hispanics/Latinos, who have a high burden of obesity and diabetes.

OBJECTIVE

In a cross-sectional analysis, we sought to examine the relationships of 3 healthy dietary patterns-the alternate Mediterranean diet (aMED), the Healthy Eating Index (HEI)-2015, and the healthful plant-based diet index (hPDI)-with the gut microbiome in US Hispanic/Latino adults, and to study the association of diet-related species with cardiometabolic traits.

METHODS

The Hispanic Community Health Study/Study of Latinos is a multi-site community-based cohort. At baseline (2008-2011), diet was assessed by using 2, 24-hour recalls. Shotgun sequencing was performed on stool samples collected in 2014-17 (n = 2444). Analysis of Compositions of Microbiomes 2 (ANCOM2) was used to identify the associations of dietary pattern scores with gut microbiome species and functions, adjusting for sociodemographic, behavioral, and clinical covariates.

RESULTS

Better diet quality according to multiple healthy dietary patterns was associated with a higher abundance of species from class Clostridia, including [Eubacterium] eligens, Butyrivibrio crossotus, and Lachnospiraceae bacterium TF01-11, but functions related to better diet quality differed for the dietary patterns (e.g., aMED with pyruvate:ferredoxin oxidoreductase, hPDI with L-arabinose/lactose transport). Poorer diet quality was associated with a higher abundance of Acidaminococcus intestini and with functions of manganese/iron transport, adhesin protein transport, and nitrate reduction. Some healthy diet pattern-enriched Clostridia species were related to more favorable cardiometabolic traits such as lower triglycerides and waist-to-hip ratio.

CONCLUSIONS

Healthy dietary patterns in this population are associated with a higher abundance of fiber-fermenting Clostridia species in the gut microbiome, consistent with previous studies in other racial/ethnic groups. Gut microbiota may be involved in the beneficial effect of higher diet quality on cardiometabolic disease risk.

Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method

An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient's age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.

Greater alteration of gut microbiota occurs in childhood obesity than in adulthood obesity

The children's gut microbiota, associated with the development of obesity, is in maturation. The impact of obesity on the gut microbiota in childhood could have a more significant effect than on adulthood and eventually be lifelong lasting, but it has been rarely studied. Aimed to discover the difference in gut microbiota between children and adults with obesity, we collected published amplicon sequencing data from National Center for Biotechnology Information (NCBI) and re-analyzed them using a uniform bioinformatic pipeline, as well as predicted the obesity using gut microbiota based on the random forest model. Summarizing common points among these cohorts, we found that the gut microbiota had a significant difference between children with and without obesity, but this difference was not observed in adult cohorts. Based on the random forest model, it was more challenging to predict childhood obesity using gut microbiota than adulthood obesity. Our results suggest that gut microbiota in childhood is more easily affected than in adulthood. Early intervention for childhood obesity is essential to improve children's health and lifelong gut microbiota-related health.

Maternal fiber deprivation alters microbiota in offspring, resulting in low-grade inflammation and predisposition to obesity

Diet, especially fiber content, plays an important role in sustaining a healthy gut microbiota, which promotes intestinal and metabolic health. Another major determinant of microbiota composition is the specific microbes that are acquired early in life, especially maternally. Consequently, we hypothesized that alterations in maternal diet during lactation might lastingly impact the microbiota composition and health status of offspring. Accordingly, we observed that feeding lactating dams low-fiber diets resulted in offspring with lasting microbiota dysbiosis, including reduced taxonomic diversity and increased abundance of Proteobacteria species, despite the offspring consuming a fiber-rich diet. Such microbiota dysbiosis was associated with increased encroachment of bacteria into inner mucus layers, low-grade gut inflammation, and a dramatically exacerbated microbiota-dependent increase in adiposity following exposure to an obesogenic diet. Thus, maternal diet is a critical long-lasting determinant of offspring microbiota composition, impacting gut health and proneness to obesity and its associated disorders.

Establishing genetic manipulation for novel strains of human gut bacteria

Recent years have seen the development of high-accuracy and high-throughput genetic manipulation techniques, which have greatly improved our understanding of genetically tractable microbes. However, challenges remain in establishing genetic manipulation techniques in novel organisms, owing largely to exogenous DNA defence mechanisms, lack of selectable markers, lack of efficient methods to introduce exogenous DNA and an inability of genetic vectors to replicate in their new host. In this review, we describe some of the techniques that are available for genetic manipulation of novel microorganisms. While many reviews exist that focus on the final step in genetic manipulation, the editing of recipient DNA, we particularly focus on the first step in this process, the transfer of exogenous DNA into a strain of interest. Examples illustrating the use of these techniques are provided for a selection of human gut bacteria in which genetic tractability has been established, such as Bifidobacterium, Bacteroides and Roseburia. Ultimately, this review aims to provide an information source for researchers interested in developing genetic manipulation techniques for novel bacterial strains, particularly those of the human gut microbiota.

Quorum sensing-based interactions among drugs, microbes, and diseases

Many diseases and health conditions are closely related to various microbes, which participate in complex interactions with diverse drugs; nonetheless, the detailed targets of such drugs remain to be elucidated. Many existing studies have reported causal associations among drugs, gut microbes, or diseases, calling for a workflow to reveal their intricate interactions. In this study, we developed a systematic workflow comprising three modules to construct a Quorum Sensing-based Drug-Microbe-Disease (QS-DMD) database ( http://www.qsdmd.lbci.net/ ), which includes diverse interactions for more than 8,000 drugs, 163 microbes, and 42 common diseases. Potential interactions between microbes and more than 8,000 drugs have been systematically studied by targeting microbial QS receptors combined with a docking-based virtual screening technique and in vitro experimental validations. Furthermore, we have constructed a QS-based drug-receptor interaction network, proposed a systematic framework including various drug-receptor-microbe-disease connections, and mapped a paradigmatic circular interaction network based on the QS-DMD, which can provide the underlying QS-based mechanisms for the reported causal associations. The QS-DMD will promote an understanding of personalized medicine and the development of potential therapies for diverse diseases. This work contributes to a paradigm for the construction of a molecule-receptor-microbe-disease interaction network for human health that may form one of the key knowledge maps of precision medicine in the future.

Distinct Gut Microbiome Induced by Different Feeding Regimes in Weaned Piglets

It is well accepted that the gut microbiota of breast-fed (BF) and formula-fed (FF) infants are significantly different. However, there is still a limited number of studies comparing the gut microbiota of BF and FF piglets, despite increasing numbers of FF piglets in the modern pig industry. The present study identified the differences in gut microbiota composition between BF- and FF-weaned Rongchang piglets at 30 days old, using pair-end sequencing on the Illumina HiSeq 2500 platform. The BF piglets had lower microbiota diversities than FF piglets (p < 0.05), and the community structures were well clustered as a result of each feeding pattern. Firmicutes and Bacteroidetes represented the most dominant phyla, and Ruminococcus, Prevotella, and Gemmiger were prominent genera in all piglets. Ruminococcus, Prevotella, Oscillospira, Eubacterium, Gemmiger, Dorea, and Lactobacillus populations were significantly higher, while Treponema and Coprococcus were significantly lower in BF piglets compared to FF piglets (p < 0.05). The metabolism pathways in the BF piglets were significantly different from FF piglets, which included carbohydrate and amino acid metabolism (p < 0.05). In addition, the top 10 abundance of microbiota were more or less significantly associated with the two phenotypes (p < 0.05). Collectively, these findings provide probable explanations for the importance of BF in neonates and support a theoretical basis for feeding regimes in indigenous Chinese piglets.

Fecal Bile Acids and Neutral Sterols Are Associated with Latent Microbial Subgroups in the Human Gut

Bile acids, neutral sterols, and the gut microbiome are intricately intertwined and each affects human health and metabolism. However, much is still unknown about this relationship. This analysis included 1280 participants of the KORA FF4 study. Fecal metabolites (primary and secondary bile acids, plant and animal sterols) were analyzed using a metabolomics approach. Dirichlet regression models were used to evaluate associations between the metabolites and twenty microbial subgroups that were previously identified using latent Dirichlet allocation. Significant associations were identified between 12 of 17 primary and secondary bile acids and several of the microbial subgroups. Three subgroups showed largely positive significant associations with bile acids, and six subgroups showed mostly inverse associations with fecal bile acids. We identified a trend where microbial subgroups that were previously associated with "healthy" factors were here inversely associated with fecal bile acid levels. Conversely, subgroups that were previously associated with "unhealthy" factors were positively associated with fecal bile acid levels. These results indicate that further research is necessary regarding bile acids and microbiota composition, particularly in relation to metabolic health.

Evaluating the clinical relevance of the enterotypes in the Estonian microbiome cohort

Human gut microbiome is subject to high inter-individual and temporal variability, which complicates building microbiome-based applications, including applications that can be used to improve public health. Categorizing the microbiome profiles into a small number of distinct clusters, such as enterotyping, has been proposed as a solution that can ameliorate these shortcomings. However, the clinical relevance of the enterotypes is poorly characterized despite a few studies marking the potential for using the enterotypes for disease diagnostics and personalized nutrition. To gain a further understanding of the clinical relevance of the enterotypes, we used the Estonian microbiome cohort dataset (n = 2,506) supplemented with diagnoses and drug usage information from electronic health records to assess the possibility of using enterotypes for disease diagnostics, detecting disease subtypes, and evaluating the susceptibility for developing a condition. In addition to the previously established 3-cluster enterotype model, we propose a 5-cluster community type model based on our data, which further separates the samples with extremely high Bacteroides and Prevotella abundances. Collectively, our systematic analysis including 231 phenotypic factors, 62 prevalent diseases, and 33 incident diseases greatly expands the knowledge about the enterotype-specific characteristics; however, the evidence suggesting the practical use of enterotypes in clinical practice remains scarce.

The Ameliorative Effect of COST on Diet-Induced Lipid Metabolism Disorders by Regulating Intestinal Microbiota

(1) Background: Chitosan oligosaccharides, with an average molecular weight ≤ 1000 Da (COST), is a natural marine product that has the potential to improve intestinal microflora and resist lipid metabolism disorders. (2) Methods: First, by establishing a mice model of lipid metabolism disorder induced by a high fat and high sugar diet, it is proven that COST can reduce lipid metabolism disorder, which may play a role in regulating intestinal microorganisms. Then, the key role of COST in the treatment of intestinal microorganisms is further confirmed through the method of COST-treated feces and fecal bacteria transplantation. (3) Conclusions: intestinal microbiota plays a key role in COST inhibition of lipid metabolism disorder induced by a high fat and high sugar diet. In particular, COST may play a central regulatory role in microbiota, including Bacteroides, Akkermansia, and Desulfovibrio. Taken together, our work suggests that COST may improve the composition of gut microbes, increase the abundance of beneficial bacteria, improve lipid metabolism disorders, and inhibit the development of metabolic disorders.

Predicting the Mortality of ICU Patients by Topic Model with Machine-Learning Techniques

Predicting clinical patients’ vital signs is a leading critical issue in intensive care units (ICUs) related studies. Early prediction of the mortality of ICU patients can reduce the overall mortality and cost of complication treatment. Some studies have predicted mortality based on electronic health record (EHR) data by using machine learning models. However, the semi-structured data (i.e., patients’ diagnosis data and inspection reports) is rarely used in these models. This study utilized data from the Medical Information Mart for Intensive Care III. We used a Latent Dirichlet Allocation (LDA) model to classify text in the semi-structured data of some particular topics and established and compared the classification and regression trees (CART), logistic regression (LR), multivariate adaptive regression splines (MARS), random forest (RF), and gradient boosting (GB). A total of 46,520 ICU Patients were included, with 11.5% mortality in the Medical Information Mart for Intensive Care III group. Our results revealed that the semi-structured data (diagnosis data and inspection reports) of ICU patients contain useful information that can assist clinical doctors in making critical clinical decisions. In addition, in our comparison of five machine learning models (CART, LR, MARS, RF, and GB), the GB model showed the best performance with the highest area under the receiver operating characteristic curve (AUROC) (0.9280), specificity (93.16%), and sensitivity (83.25%). The RF, LR, and MARS models showed better performance (AUROC are 0.9096, 0.8987, and 0.8935, respectively) than the CART (0.8511). The GB model showed better performance than other machine learning models (CART, LR, MARS, and RF) in predicting the mortality of patients in the intensive care unit. The analysis results could be used to develop a clinically useful decision support system.

A Guide to Dietary Pattern-Microbiome Data Integration

The human gut microbiome is linked to metabolic and cardiovascular disease risk. Dietary modulation of the human gut microbiome offers an attractive pathway to manipulate the microbiome to prevent microbiome-related disease. However, this promise has not been realized. The complex system of diet and microbiome interactions is poorly understood. Integrating observational human diet and microbiome data can help researchers and clinicians untangle the complex systems of interactions that predict how the microbiome will change in response to foods. The use of dietary patterns to assess diet-microbiome relations holds promise to identify interesting associations and result in findings that can directly translate into actionable dietary intake recommendations and eating plans. In this article, we first highlight the complexity inherent in both dietary and microbiome data and introduce the approaches generally used to explore diet and microbiome simultaneously in observational studies. Second, we review the food group and dietary pattern-microbiome literature focusing on dietary complexity-moving beyond nutrients. Our review identified a substantial and growing body of literature that explores links between the microbiome and dietary patterns. However, there was very little standardization of dietary collection and assessment methods across studies. The 54 studies identified in this review used ≥7 different methods to assess diet. Coupled with the variation in final dietary parameters calculated from dietary data (e.g., dietary indices, dietary patterns, food groups, etc.), few studies with shared methods and assessment techniques were available for comparison. Third, we highlight the similarities between dietary and microbiome data structures and present the possibility that multivariate and compositional methods, developed initially for microbiome data, could have utility when applied to dietary data. Finally, we summarize the current state of the art for diet-microbiome data integration and highlight ways dietary data could be paired with microbiome data in future studies to improve the detection of diet-microbiome signals.

Negative binomial factor regression with application to microbiome data analysis

The human microbiome provides essential physiological functions and helps maintain host homeostasis via the formation of intricate ecological host‐microbiome relationships. While it is well established that the lifestyle of the host, dietary preferences, demographic background, and health status can influence microbial community composition and dynamics, robust generalizable associations between specific host‐associated factors and specific microbial taxa have remained largely elusive. Here, we propose factor regression models that allow the estimation of structured parsimonious associations between host‐related features and amplicon‐derived microbial taxa. To account for the overdispersed nature of the amplicon sequencing count data, we propose negative binomial reduced rank regression (NB‐RRR) and negative binomial co‐sparse factor regression (NB‐FAR). While NB‐RRR encodes the underlying dependency among the microbial abundances as outcomes and the host‐associated features as predictors through a rank‐constrained coefficient matrix, NB‐FAR uses a sparse singular value decomposition of the coefficient matrix. The latter approach avoids the notoriously difficult joint parameter estimation by extracting sparse unit‐rank components of the coefficient matrix sequentially, effectively delivering interpretable bi‐clusters of taxa and host‐associated factors. To solve the nonconvex optimization problems associated with these factor regression models, we present a novel iterative block‐wise majorization procedure. Extensive simulation studies and an application to the microbial abundance data from the American Gut Project (AGP) demonstrate the efficacy of the proposed procedure. In the AGP data, we identify several factors that strongly link dietary habits and host life style to specific microbial families.

Alterations of the Gut Microbiome in Recurrent Malignant Gliomas Patients Received Bevacizumab and Temozolomide Combination Treatment and Temozolomide Monotherapy

This case-control study explored compositions of gut microbiome in recurrent malignant gliomas patients who had received bevacizumab and Temozolomide combination treatment and Temozolomide monotherapy. We investigated gut microbiota communities in feces of 29 recurrent malignant gliomas patients received combination treatment with bevacizumab and Temozolomide (Group 1) and monotherapy with Temozolomide alone (Group 2). We took advantage of the high-throughput Illumina Miseq sequencing technology by targeting the third and fourth hypervariable (V3-V4) regions of the 16S ribosomal RNA (rRNA) gene. We found that the structures and richness of the fecal microbiota in Group 1 were different from Group 2 with LEfSe analysis. The fecal microbiota in both Group 1 and Group 2 were mainly composed by Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria. However, Group 1 patients had higher relative abundance of Firmicutes, Bacteroidetes, Actinobacteria and lower relative abundance of Bacteroidetes and Cyanobacteria in their fecal microbiota than that in Group 2 patients. To evaluate bevacizumab involved post-treatment state of the fecal microbiota profile, we used random forest predictive model and ensembled decision trees with an AUC of 0.54. This study confirmed that the gut microbiota was different in recurrent malignant gliomas patients received the combination therapy of bevacizumab and Temozolomide compared with Temozolomide monotherapy. Our discover can help better understand the influence of bevacizumab related treatment on recurrent malignant gliomas patients. Therefore, this finding may also support the potentially therapeutic options for recurrent malignant gliomas patients such as fecal microbiota transplant.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12088-021-00962-2.

Stratification of the Gut Microbiota Composition Landscape across the Alzheimer's Disease Continuum in a Turkish Cohort

Alzheimer's disease (AD) is a heterogeneous disorder that spans a continuum with multiple phases, including preclinical, mild cognitive impairment, and dementia. Unlike for most other chronic diseases, human studies reporting on AD gut microbiota in the literature are very limited. With the scarcity of approved drugs for AD therapies, the rational and precise modulation of gut microbiota composition using diet and other tools is a promising approach to the management of AD. Such an approach could be personalized if an AD continuum can first be deconstructed into multiple strata based on specific microbiota features by using single or multiomics techniques. However, stratification of AD gut microbiota has not been systematically investigated before, leaving an important research gap for gut microbiota-based therapeutic approaches. Here, we analyze 16S rRNA amplicon sequencing of stool samples from 27 patients with mild cognitive impairment, 47 patients with AD, and 51 nondemented control subjects by using tools compatible with the compositional nature of microbiota. To stratify the AD gut microbiota community, we applied four machine learning techniques, including partitioning around the medoid clustering and fitting a probabilistic Dirichlet mixture model, the latent Dirichlet allocation model, and we performed topological data analysis for population-scale microbiome stratification based on the Mapper algorithm. These four distinct techniques all converge on Prevotella and Bacteroides stratification of the gut microbiota across the AD continuum, while some methods provided fine-scale resolution in stratifying the community landscape. Finally, we demonstrate that the signature taxa and neuropsychometric parameters together robustly classify the groups. Our results provide a framework for precision nutrition approaches aiming to modulate the AD gut microbiota. IMPORTANCE The prevalence of AD worldwide is estimated to reach 131 million by 2050. Most disease-modifying treatments and drug trials have failed, due partly to the heterogeneous and complex nature of the disease. Recent studies demonstrated that gut dybiosis can influence normal brain function through the so-called "gut-brain axis." Modulation of the gut microbiota, therefore, has drawn strong interest in the clinic in the management of the disease. However, there is unmet need for microbiota-informed stratification of AD clinical cohorts for intervention studies aiming to modulate the gut microbiota. Our study fills in this gap and draws attention to the need for microbiota stratification as the first step for microbiota-based therapy. We demonstrate that while Prevotella and Bacteroides clusters are the consensus partitions, the newly developed probabilistic methods can provide fine-scale resolution in partitioning the AD gut microbiome landscape.

Nutrition Concepts for the Treatment of Obesity in Adults

Obesity caused by a positive energy balance is a serious health burden. Studies have shown that obesity is the major risk factor for many diseases like type 2 diabetes mellitus, coronary heart diseases, or various types of cancer. Therefore, the prevention and treatment of increased body weight are key. Different evidence-based treatment approaches considering weight history, body mass index (BMI) category, and co-morbidities are available: lifestyle intervention, formula diet, drugs, and bariatric surgery. For all treatment approaches, behaviour change techniques, reduction in energy intake, and increasing energy expenditure are required. Self-monitoring of diet and physical activity provides an effective behaviour change technique for weight management. Digital tools increase engagement rates for self-monitoring and have the potential to improve weight management. The objective of this narrative review is to summarize current available treatment approaches for obesity, to provide a selective overview of nutrition trends, and to give a scientific viewpoint for various nutrition concepts for weight loss.

Gut Microbiome-Based Diagnostic Model to Predict Diabetes Mellitus

The aim of this study was to determine the diversity of intestinal microflora and its correlation with clinical parameters in diabetic patients and healthy subjects and to assess the importance of intestinal flora in patients with diabetes. Forty-four patients with diabetes were included. The control group included 47 healthy people. Their data, biochemical indicators and results from 16S rRNA sequencing of their fecal samples were collected. Compared with the healthy population, the intestinal flora of the diabetic patients was obviously abnormal. Within the diabetes group, the abundances of the genera Faecalibacterium, Prevotella, and Roseburia were higher, and the abundances of the genera Shigella and Bifidobacterium were lower. In the correlation analysis between bacteria and clinical indicators, it was found that the genera Veillonella and unclassified_Enterobacteriaceae were negatively related to blood glucose, while the genera Phascolarctobacterium, unidentified_Bacteroidales and Prevotella were significantly positively correlated with fasting blood glucose. Twelve microbial markers were detected in the random forest model, and the area under the curve (AUC) was 84.1%. This index was greater than the diagnostic effect of fasting blood glucose. This was also supported by the joint diagnostic model of microorganisms and clinical indicators. In addition, the intestinal flora significantly improved the diagnosis of diabetes. In conclusion, it can be concluded from these results that intestinal flora is essential for the occurrence and development of diabetes, which seems to be as important as blood glucose itself.

Abbreviations: PCoA: principal coordinate analysis; NMDS: non econometric multidimensional scaling analysis; LEfSe: linear discriminant analysis effect size; LDA: linear discriminant analysis; POD: probability of disease; BMI: body mass index; DCA: decision curve analysis

Fruit and Vegetable Supplemented Diet Modulates the Pig Transcriptome and Microbiome after a Two-Week Feeding Intervention

A study was conducted to determine the effects of a diet supplemented with fruits and vegetables (FV) on the host whole blood cell (WBC) transcriptome and the composition and function of the intestinal microbiome. Nine six-week-old pigs were fed a pig grower diet alone or supplemented with lyophilized FV equivalent to half the daily recommended amount prescribed for humans by the Dietary Guideline for Americans (DGA) for two weeks. Host transcriptome changes in the WBC were evaluated by RNA sequencing. Isolated DNA from the fecal microbiome was used for 16S rDNA taxonomic analysis and prediction of metabolomic function. Feeding an FV-supplemented diet to pigs induced differential expression of several genes associated with an increase in B-cell development and differentiation and the regulation of cellular movement, inflammatory response, and cell-to-cell signaling. Linear discriminant analysis effect size (LEfSe) in fecal microbiome samples showed differential increases in genera from Lachnospiraceae and Ruminococcaceae families within the order Clostridiales and Erysipelotrichaceae family with a predicted reduction in rgpE-glucosyltransferase protein associated with lipopolysaccharide biosynthesis in pigs fed the FV-supplemented diet. These results suggest that feeding an FV-supplemented diet for two weeks modulated markers of cellular inflammatory and immune function in the WBC transcriptome and the composition of the intestinal microbiome by increasing the abundance of bacterial taxa that have been associated with improved intestinal health.

Association between gut microbiota and elevated serum urate in two independent cohorts

OBJECTIVES

Hyperuricemia is a precursor to gout and is often present in other metabolic diseases that are promoted by microbiome dysbiosis; however, no study has examined the association of gut microbiota with hyperuricemia and serum urate in humans.

METHODS

Study participants were derived from a community-based observational study, the Xiangya Osteoarthritis Study (discovery cohort). Hyperuricemia was defined as the presence of serum urate level >357 μmol/L for women and >416 μmol/L for men. Gut microbiota was analyzed using 16S rRNA sequencing from stool samples. We examined the relation of microbiota dysbiosis (i.e., richness, diversity, composition, and relative abundance of microbiota taxa) and predicted functional pathways to prevalent hyperuricemia and serum urate levels. We verified the associations in an independent observational study, the Step Study (validation cohort).

RESULTS

The discovery cohort consisted of 1,392 rural participants (mean age: 61.3 years; women: 57.4%; hyperuricemia: 17.2%). Participants with hyperuricemia had decreased richness and diversity, altered composition of microbiota, and lower relative abundances of genus Coprococcus compared with those with normouricemia. Predicted Kyoto Encyclopedia of Genes and Genomes metabolism pathways belonged to amino acid and nucleotide metabolisms were significantly altered in individuals with hyperuricemia compared with those with normouricemia. Gut microbiota richness, diversity and low relative abundances of genus Coprococcus were also associated with high levels of serum urate. These findings were replicated in the validation cohort with 480 participants.

CONCLUSIONS

Gut microbiota dysbiosis was associated with elevated serum urate levels. Our study raises the possibility that microbiota dysbiosis may modulate serum urate levels.

Effects of Different Concentrations of Ganpu Tea on Fecal Microbiota and Short Chain Fatty Acids in Mice

Ganpu tea is composed of tangerine peel and Pu-erh tea. Current research suggests that both products can interact with gut microbes and thus affect health. However, as a kind of compound health food, little information is available about the effect of Ganpu tea on intestinal microorganisms. In this study, the basic physiological parameters (body weight, white adipose tissue and serum fat), the regulation of intestinal microorganisms and content of short-chain fatty acids (SCFAs) in feces of healthy mice were studied. The Ganpu tea can reduce the weight gain of mice and the increase in white adipose tissue (p < 0.01). After the intake of Ganpu tea, the abundance of Bacteroidetes increased (p < 0.05), whereas that of Firmicutes decreased (p < 0.01), indicating the latent capacity of Ganpu tea in adjusting the gut microbiota. Moreover, Ganpu tea differentially affected the content of different types of SCFAs in feces. Ganpu tea at the lowest concentrations showed positive effects on the concentrations of SCFAs such as acetic acid and propionic acid, whereas the concentration of butyric acid was decreased. For branched short-chain fatty acids (BSCFAs) such as isobutyric acid, isovaleric acid, etc., Ganpu tea reduced their concentrations. Our results indicated that Ganpu tea may have positive effects on preventing obesity in humans, but further research is needed before introducing such dietary therapy.

Altered Fecal Microbiota Composition in Older Adults With Frailty

Objective

Frailty is a common geriatric syndrome that is diagnosed and staged based mainly on symptoms. We aimed to evaluate frailty-related alterations of the intestinal permeability and profile fecal microbiota of healthy and frail older adults to identify microbial biomarkers of this syndrome.

Methods

We collected serum and fecal samples from 94 community-dwelling older adults, along with anthropometric, medical, mental health, and lifestyle data. Serum inflammatory cytokines IL-6 and HGMB1 and the intestinal permeability biomarker zonulin were measured using enzyme-linked immunosorbent assays. The 16S rRNA amplicon sequencing method was performed to determine the fecal composition of fecal microbiota. We analyzed the diversity and composition differences of the gut microbiota in the two groups and assessed the relationship between the changes in microbiota structure and clinical biomarkers.

Results

Older adults with frailty showed higher concentrations of IL-6, HGMB1, and zonulin. Although there were no statistically significant differences in the diversity index and evenness indices or species richness of fecal microbiota between the two groups, we found significant microbiota structure differences. Compared with the control group, fecal samples from the frail group had higher levels of Akkermansia, Parabacteroides, and Klebsiella and lower levels of the commensal genera Faecalibacterium, Prevotella, Roseburia, Megamonas, and Blautia. Spearman’s correlation analysis showed that the intergenus interactions were more common in healthy controls than older adults with frailty. Escherichia/Shigella, Pyramidobacter, Alistipes, and Akkermansia were positively correlated with IL-6, while Faecalibacterium, Prevotella, and Roseburia were negatively correlated with IL-6. Alistipes were found to be positively correlated with HGMB1. Akkermansia and Alistipes were linked to the increased serum level of inflammatory factors and intestinal permeability.

Conclusions

Frailty is associated with differences in the composition of fecal microbiota. These findings might aid in the development of probiotics or microbial-based therapies for frailty.